Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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New Feature Selection Method for Text Categorization

  • Wang, Xingfeng (Information Engineering College, Eastern Liaoning University) ;
  • Kim, Hee-Cheol (Department of Computer Engineering/Institute of Digital Anti-Aging Healthcare (IDA), Inje University)
  • Received : 2017.03.09
  • Accepted : 2017.03.17
  • Published : 2017.03.31
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Abstract

The preferred feature selection methods for text classification are filter-based. In a common filter-based feature selection scheme, unique scores are assigned to features; then, these features are sorted according to their scores. The last step is to add the top-N features to the feature set. In this paper, we propose an improved global feature selection scheme wherein its last step is modified to obtain a more representative feature set. The proposed method aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in the proposed method to label features according to their discriminative power on classes; these labels are used while producing the feature sets. Experimental results obtained using the well-known 20 Newsgroups and Reuters-21578 datasets with the k-nearest neighbor algorithm and a support vector machine indicate that the proposed method improves the classification performance in terms of a widely known metric ($F_1$).

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